{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:52:16Z","timestamp":1793404336799,"version":"4.5.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s10439-025-03898-6","type":"journal-article","created":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T19:22:04Z","timestamp":1763320924000},"page":"531-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["EFD in Comparison with EWT for Synthetic and EEG Signal Decomposition and Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment"],"prefix":"10.1007","volume":"54","author":[{"given":"Saba Haji Molla","family":"Rabie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8381-316X","authenticated-orcid":false,"given":"Sedigheh","family":"Ghofrani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamideh","family":"Barghamadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Eslami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"issue":"14","key":"3898_CR1","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.3390\/s23146434","volume":"23","author":"A Chaddad","year":"2023","unstructured":"Chaddad, A., et al. Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques. Sensors. 23(14):6434, 2023. https:\/\/doi.org\/10.3390\/s23146434.","journal-title":"Sensors"},{"key":"3898_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.neubiorev.2020.07.020","volume":"118","author":"U Ghani","year":"2020","unstructured":"Ghani, U., et al. ERP-based measures of cognitive workload: A review. Neuroscience & Biobehavioral Reviews. 118:18\u201326, 2020. https:\/\/doi.org\/10.1016\/j.neubiorev.2020.07.020.","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"3898_CR3","unstructured":"Reaves, J., et al., Assessment and application of EEG: A literature review. Journal of Applied Bioinformatics & Computational Biology, 2021. 10(7)."},{"issue":"19","key":"3898_CR4","doi-asserted-by":"publisher","first-page":"10889","DOI":"10.3390\/ijms221910889","volume":"22","author":"P Monllor","year":"2021","unstructured":"Monllor, P., et al. Electroencephalography as a non-invasive biomarker of Alzheimer\u2019s disease: A forgotten candidate to substitute CSF molecules? International Journal of Molecular Sciences. 22(19):10889, 2021. https:\/\/doi.org\/10.3390\/ijms221910889.","journal-title":"International Journal of Molecular Sciences"},{"issue":"2","key":"3898_CR5","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/e22020239","volume":"22","author":"J Sun","year":"2020","unstructured":"Sun, J., et al. Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer\u2019s disease: a review. Entropy. 22(2):239, 2020. https:\/\/doi.org\/10.3390\/e22020239.","journal-title":"Entropy"},{"issue":"3","key":"3898_CR6","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1177\/155005949102200309","volume":"22","author":"DB Hier","year":"1991","unstructured":"Hier, D. B., et al. Quantitative measurement of delta activity in Alzheimer\u2019s disease. Clinical electroencephalography. 22(3):178\u2013182, 1991. https:\/\/doi.org\/10.1177\/155005949102200309.","journal-title":"Clinical electroencephalography"},{"key":"3898_CR7","doi-asserted-by":"publisher","unstructured":"Ieracitano, C., et al., A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Networks, 2020. 123: p. 176-190.https:\/\/doi.org\/10.1016\/j.neunet.2019.12.006.","DOI":"10.1016\/j.neunet.2019.12.006"},{"key":"3898_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102338","volume":"65","author":"MS Safi","year":"2021","unstructured":"Safi, M. S., and S. M. M. Safi. Early detection of Alzheimer\u2019s disease from EEG signals using Hjorth parameters. Biomedical Signal Processing and Control.65:102338, 2021. https:\/\/doi.org\/10.1016\/j.bspc.2020.102338.","journal-title":"Biomedical Signal Processing and Control"},{"issue":"9","key":"3898_CR9","doi-asserted-by":"publisher","first-page":"1966","DOI":"10.1109\/TNSRE.2020.3013429","volume":"28","author":"S Siuly","year":"2020","unstructured":"Siuly, S., et al. A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28(9):1966\u20131976, 2020. https:\/\/doi.org\/10.1109\/TNSRE.2020.3013429.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"1","key":"3898_CR10","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","volume":"13","author":"S Grueso","year":"2021","unstructured":"Grueso, S., and R. Viejo-Sobera. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a systematic review. Alzheimer\u2019s research & therapy. 13(1):162, 2021. https:\/\/doi.org\/10.1186\/s13195-021-00900-w.","journal-title":"Alzheimer's research & therapy"},{"key":"3898_CR11","doi-asserted-by":"publisher","unstructured":"Akan, A. and O.K. Cura, Time\u2013frequency signal processing: Today and future. Digital Signal Processing, 2021. 119: p. 103216.https:\/\/doi.org\/10.1016\/j.dsp.2021.103216.","DOI":"10.1016\/j.dsp.2021.103216"},{"key":"3898_CR12","doi-asserted-by":"publisher","unstructured":"Amini, M., et al., Diagnosis of Alzheimer\u2019s disease by time-dependent power spectrum descriptors and a convolutional neural network using EEG signal. Computational and Mathematical Methods in Medicine, 2021. 2021.https:\/\/doi.org\/10.1155\/2021\/5511922.","DOI":"10.1155\/2021\/5511922"},{"key":"3898_CR13","doi-asserted-by":"publisher","unstructured":"Pirrone, D., et al., EEG signal processing and supervised machine learning to early diagnose alzheimer\u2019s disease. Applied sciences, 2022. 12(11): p. 5413.https:\/\/doi.org\/10.3390\/app12115413.","DOI":"10.3390\/app12115413"},{"key":"3898_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fnana.2022.936025","volume":"16","author":"G Leisman","year":"2022","unstructured":"Leisman, G., and R. Melillo. Front and center: Maturational dysregulation of frontal lobe functional neuroanatomic connections in attention deficit hyperactivity disorder. Frontiers in Neuroanatomy.16:936025, 2022. https:\/\/doi.org\/10.3389\/fnana.2022.936025.","journal-title":"Frontiers in Neuroanatomy"},{"key":"3898_CR15","unstructured":"Rubella, D.C.a.M.K., Bio Signal ( EEG ) Using Empirical Wavelet Transform In Time Frequency Analysis. Electronics and Communication Engineering 2018: p. 23-29."},{"key":"3898_CR16","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSP.2020.2992865","volume":"68","author":"J Shi","year":"2020","unstructured":"Shi, J., et al. Novel short-time fractional Fourier transform: Theory, implementation, and applications. IEEE Transactions on Signal Processing. 68:3280\u20133295, 2020. https:\/\/doi.org\/10.1109\/TSP.2020.2992865.","journal-title":"IEEE Transactions on Signal Processing"},{"issue":"4","key":"3898_CR17","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1137\/1031129","volume":"31","author":"CE Heil","year":"1989","unstructured":"Heil, C. E., and D. F. Walnut. Continuous and discrete wavelet transforms. SIAM review. 31(4):628\u2013666, 1989. https:\/\/doi.org\/10.1137\/1031129.","journal-title":"SIAM review"},{"issue":"3","key":"3898_CR18","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1112\/jlms\/s1-1.1.19","volume":"1","author":"JE Littlewood","year":"1931","unstructured":"Littlewood, J. E., and R. E. Paley. Theorems on Fourier series and power series. Journal of the London Mathematical Society. 1(3):230\u2013233, 1931. https:\/\/doi.org\/10.1112\/jlms\/s1-1.1.19.","journal-title":"Journal of the London Mathematical Society"},{"key":"3898_CR19","doi-asserted-by":"crossref","unstructured":"Meyer, Y., Wavelets and Operators: Volume 1. 1992: Cambridge University Press.","DOI":"10.1017\/CBO9780511623820"},{"issue":"4","key":"3898_CR20","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/79.91217","volume":"8","author":"O Rioul","year":"1991","unstructured":"Rioul, O., and M. Vetterli. Wavelets and signal processing. IEEE Signal Processing Magazine. 8(4):14\u201338, 1991. https:\/\/doi.org\/10.1109\/79.91217.","journal-title":"IEEE Signal Processing Magazine"},{"key":"3898_CR21","doi-asserted-by":"publisher","unstructured":"Ghofrani, H.A.S., Empirical Wavelet Transform; Stationary and Nonstationary Signals. Journal of Electronic & Information Systems| Volume, 2019. 1(02). https:\/\/doi.org\/10.30564\/jeisr.v1i2.1008.","DOI":"10.30564\/jeisr.v1i2.1008"},{"issue":"09","key":"3898_CR22","doi-asserted-by":"publisher","first-page":"2250042","DOI":"10.1142\/S0129065722500423","volume":"32","author":"OK Cura","year":"2022","unstructured":"Cura, O. K., et al. Detection of alzheimer\u2019s dementia by using signal decomposition and machine learning methods. International journal of neural systems. 32(09):2250042, 2022. https:\/\/doi.org\/10.1142\/S0129065722500423.","journal-title":"International journal of neural systems"},{"key":"3898_CR23","doi-asserted-by":"publisher","unstructured":"\u015eEKER, M. and M.S. \u00d6ZERDEM, Automated Detection of Alzheimer\u2019s Disease using raw EEG time series via. DWT-CNN model. Dicle \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi M\u00fchendislik Dergisi, 2023. 13(4): p. 673-684. https:\/\/doi.org\/10.24012\/dumf 1197722.","DOI":"10.24012\/dumf"},{"issue":"4","key":"3898_CR24","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac84ac","volume":"19","author":"J Cao","year":"2022","unstructured":"Cao, J., et al. Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer\u2019s disease. Journal of Neural Engineering.19(4):046034, 2022. https:\/\/doi.org\/10.1088\/1741-2552\/ac84ac.","journal-title":"Journal of Neural Engineering"},{"key":"3898_CR25","doi-asserted-by":"publisher","unstructured":"Huang, N.E., Hilbert-Huang transform and its applications. Vol. 16. 2014: World Scientific. https:\/\/doi.org\/10.1142\/8804.","DOI":"10.1142\/8804"},{"key":"3898_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/1496973","volume":"2020","author":"X Zheng","year":"2020","unstructured":"Zheng, X., et al. Collaborative sleep electroencephalogram data analysis based on improved empirical mode decomposition and clustering algorithm. Complexity. 2020:1\u201314, 2020. https:\/\/doi.org\/10.1155\/2020\/1496973.","journal-title":"Complexity"},{"issue":"16","key":"3898_CR27","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.1109\/TSP.2013.2265222","volume":"61","author":"J Gilles","year":"2013","unstructured":"Gilles, J. Empirical wavelet transform. IEEE transactions on signal processing. 61(16):3999\u20134010, 2013. https:\/\/doi.org\/10.1109\/TSP.2013.2265222.","journal-title":"IEEE transactions on signal processing"},{"key":"3898_CR28","doi-asserted-by":"publisher","unstructured":"Zhou, W., et al., Empirical Fourier decomposition. arXiv preprint arXiv:1912.00414, 2019. https:\/\/doi.org\/10.48550\/arXiv.1912.00414.","DOI":"10.48550\/arXiv.1912.00414"},{"key":"3898_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108155","volume":"163","author":"W Zhou","year":"2022","unstructured":"Zhou, W., et al. Empirical Fourier decomposition: An accurate signal decomposition method for nonlinear and non-stationary time series analysis. Mechanical Systems and Signal Processing.163:108155, 2022. https:\/\/doi.org\/10.1016\/j.ymssp.2021.108155.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"3898_CR30","doi-asserted-by":"publisher","unstructured":"Gao, R.X. and R. Yan, Wavelets: Theory and applications for manufacturing. 2010: Springer Science & Business Media. https:\/\/doi.org\/10.1007\/978-1-4419-1545-0.","DOI":"10.1007\/978-1-4419-1545-0"},{"issue":"1","key":"3898_CR31","doi-asserted-by":"publisher","first-page":"2304574","DOI":"10.1080\/21681163.2024.2304574","volume":"12","author":"A Jain","year":"2024","unstructured":"Jain, A., R. Raja, S. Srivastava, P. C. Sharma, J. Gangrade, and R. Manoj. Analysis of EEG signals and data acquisition methods: a review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 12(1):2304574, 2024. https:\/\/doi.org\/10.1080\/21681163.2024.2304574.","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization"},{"key":"3898_CR32","doi-asserted-by":"publisher","unstructured":"Mazzeo, M., D. Domenico, D. Quaranta, G. Santoro, R., An efficient automatic modal identification method based on free vibration response and enhanced Empirical Fourier Decomposition technique. Engineering Structures, 2024. 298: p. 117046. https:\/\/doi.org\/10.1016\/j.engstruct.2023.117046.","DOI":"10.1016\/j.engstruct.2023.117046"},{"key":"3898_CR33","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.isatra.2022.02.049","volume":"129","author":"J Zheng","year":"2022","unstructured":"Zheng, J., S. Cao, H. Pan, and Q. Ni. Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis. ISA transactions. 129:476\u2013492, 2022. https:\/\/doi.org\/10.1016\/j.isatra.2022.02.049.","journal-title":"ISA transactions"},{"issue":"2","key":"3898_CR34","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1177\/14759217231178653","volume":"23","author":"D Zhu","year":"2024","unstructured":"Zhu, D., et al. An enhanced empirical Fourier decomposition method for bearing fault diagnosis. Structural Health Monitoring. 23(2):903\u2013923, 2024. https:\/\/doi.org\/10.1177\/14759217231178653.","journal-title":"Structural Health Monitoring"},{"key":"3898_CR35","unstructured":"Azar, S.G., et al., From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. Transactions on Machine Learning Research."},{"key":"3898_CR36","doi-asserted-by":"crossref","unstructured":"Sadegh-Zadeh, S.A., Fakhri, E., Bahrami, M., Bagheri, E., Khamsehashari, R., Noroozian, M., et al., An approach toward artificial intelligence Alzheimer\u2019s disease diagnosis using brain signals. diagnostics, 2023. 13(3): p. 477.","DOI":"10.3390\/diagnostics13030477"},{"issue":"5","key":"3898_CR37","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1140\/epjs\/s11734-022-00753-w","volume":"232","author":"H Ferdinando","year":"2023","unstructured":"Ferdinando, H., S. Moradi, V. Korhonen, H. Helakari, V. Kiviniemi, and T. Myllyla. Spectral entropy provides separation between Alzheimer\u2019s disease patients and controls: a study of fNIRS. The European Physical Journal Special Topics. 232(5):655\u2013662, 2023. https:\/\/doi.org\/10.1140\/epjs\/s11734-022-00753-w.","journal-title":"The European Physical Journal Special Topics"},{"key":"3898_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106116","volume":"206","author":"M \u015eeker","year":"2021","unstructured":"\u015eeker, M., Y. \u00d6zbek, G. Yener, and M. S. \u00d6zerdem. Complexity of EEG dynamics for early diagnosis of Alzheimer\u2019s disease using permutation entropy neuromarker. Computer Methods and Programs in Biomedicine.206:106116, 2021.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"3898_CR39","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.mri.2023.08.001","volume":"106","author":"R Liu","year":"2024","unstructured":"Liu, R., Z. Guo, M. Li, S. Liu, Y. Zhi, Z. Jiang, et al. Lower fractional dimension in Alzheimer\u2019s disease correlates with reduced locus coeruleus signal intensity. Magnetic Resonance Imaging. 106:24\u201330, 2024. https:\/\/doi.org\/10.1016\/j.mri.2023.08.001.","journal-title":"Magnetic Resonance Imaging"},{"issue":"4","key":"3898_CR40","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.3233\/JAD-220508","volume":"91","author":"MHF Fischer","year":"2023","unstructured":"Fischer, M. H. F., I. C. Zibrandtsen, P. H\u00f8gh, and C. S. Musaeus. Systematic review of EEG coherence in Alzheimer\u2019s disease. Journal of Alzheimer\u2019s Disease. 91(4):1261\u20131272, 2023. https:\/\/doi.org\/10.3233\/JAD-220508.","journal-title":"Journal of Alzheimer's Disease"},{"key":"3898_CR41","doi-asserted-by":"publisher","unstructured":"Bunterngchit, C., T. Chearanai, and Y. Bunterngchit. Advanced EEG-Based Classification of Alzheimer's Disease Using CNN-LSTM-Attention Architecture. In 2024 22nd International Conference on Research and Education in Mechatronics (REM). 2024. IEEE. https:\/\/doi.org\/10.1109\/REM63063.2024.10735597.","DOI":"10.1109\/REM63063.2024.10735597"},{"issue":"7","key":"3898_CR42","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat, S. G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence. 11(7):674\u2013693, 1989. https:\/\/doi.org\/10.1109\/34.192463.","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"3898_CR43","volume-title":"A brief study of discrete and fast Fourier transforms\u2014the University of Chicago VIGRE REU: Chicago","author":"AV Anand","year":"2010","unstructured":"Anand, A. V. A brief study of discrete and fast Fourier transforms\u2014the University of Chicago VIGRE REU: Chicago. USA: IL, 2010."},{"key":"3898_CR44","doi-asserted-by":"publisher","unstructured":"Matei, R. Directional filter banks designed from zero-phase low-pass filters. In the 2019 International Symposium on Signals, Circuits, and Systems (ISSCS). 2019. IEEE. https:\/\/doi.org\/10.1109\/ISSCS.2019.8801750.","DOI":"10.1109\/ISSCS.2019.8801750"},{"key":"3898_CR45","doi-asserted-by":"publisher","unstructured":"Attar, E.T., Review of electroencephalography signals approaches for mental stress assessment. Neurosciences Journal, 2022. 27(4): p. 209-215. https:\/\/doi.org\/10.17712\/nsj.2022.4.20220025.","DOI":"10.17712\/nsj.2022.4.20220025"},{"key":"3898_CR46","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neurobiolaging.2019.10.004","volume":"85","author":"CSY Benwell","year":"2020","unstructured":"Benwell, C. S. Y., P. Davila-P\u00e9rez, P. J. Fried, R. N. Jones, T. G. Travison, E. Santarnecchi, et al. EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer\u2019s disease and type 2 diabetes. Neurobiology of aging. 85:83\u201395, 2020. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2019.10.004.","journal-title":"Neurobiology of aging"},{"key":"3898_CR47","doi-asserted-by":"publisher","unstructured":"Elshekhidris, I.H., Mohamed Amien M., Fragoon A., Wavelet transforms for EEG signal denoising and decomposition. International Journal of Advances in Signal and Image Sciences, 2023. 9(2): p. 11-28. https:\/\/doi.org\/10.29284\/ijasis.9.2.2023.11-28.","DOI":"10.29284\/ijasis.9.2.2023.11-28"},{"key":"3898_CR48","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco, D., M. J. Warrens, and G. Jurman. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE, and RMSE in regression analysis evaluation. Peerj computer science.7:e623, 2021. https:\/\/doi.org\/10.7717\/peerj-cs.623.","journal-title":"Peerj computer science"},{"key":"3898_CR49","doi-asserted-by":"publisher","unstructured":"Xia W, Zhang R, Zhang X, Usman M. A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals. Heliyon, 2023. 9(4). https:\/\/doi.org\/10.1016\/j.heliyon.2023.e14858.","DOI":"10.1016\/j.heliyon.2023.e14858"},{"key":"3898_CR50","doi-asserted-by":"publisher","unstructured":"Schober, C. Boer, and L.A. Schwarte, Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 2018. 126(5): p. 1763-1768. https:\/\/doi.org\/10.1213\/ANE.0000000000002864","DOI":"10.1213\/ANE.0000000000002864"},{"issue":"5","key":"3898_CR51","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1007\/s10618-023-00926-8","volume":"37","author":"M Herrmann","year":"2023","unstructured":"Herrmann, M., C. W. Tan, and G. I. Webb. Parameterizing the cost function of dynamic time warping with application to time series classification. Data Mining and Knowledge Discovery. 37(5):2024\u20132045, 2023. https:\/\/doi.org\/10.1007\/s10618-023-00926-8.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"7","key":"3898_CR52","doi-asserted-by":"publisher","first-page":"3137","DOI":"10.1080\/03610918.2021.1928193","volume":"52","author":"D Baumgartner","year":"2023","unstructured":"Baumgartner, D., and J. Kolassa. Power considerations for Kolmogorov-Smirnov and Anderson-Darling two-sample tests. Communications in Statistics-Simulation and Computation. 52(7):3137\u20133145, 2023. https:\/\/doi.org\/10.1080\/03610918.2021.1928193.","journal-title":"Communications in Statistics-Simulation and Computation"},{"key":"3898_CR53","doi-asserted-by":"publisher","unstructured":"Qi, X. and T. Guo. Instantaneous frequency estimation of the non-stationary signal. In the 4th International Conference on Information Technology and Management Innovation. 2015. Atlantis Press. https:\/\/doi.org\/10.2991\/icitmi-15.2015.189.","DOI":"10.2991\/icitmi-15.2015.189"},{"issue":"1","key":"3898_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-018-0613-y","volume":"18","author":"G Fiscon","year":"2018","unstructured":"Fiscon, G., E. Weitschek, A. Cialini, G. Felici, P. Bertolazzi, S. De Salvo, A. Bramanti, P. Bramanti, and M. C. De Cola. Combining EEG signal processing with supervised methods for Alzheimer\u2019s patients classification. BMC medical informatics and decision making. 18(1):1\u201310, 2018. https:\/\/doi.org\/10.1186\/s12911-018-0613-y.","journal-title":"BMC medical informatics and decision making"},{"key":"3898_CR55","doi-asserted-by":"publisher","unstructured":"Fiscon G, Weitschek E, Felici G, Bertolazzi P, De Cola MC. An integrated approach based on EEG signals processing combined with supervised methods to classify Alzheimer\u2019s disease patients. In the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2018. IEEE. https:\/\/doi.org\/10.1109\/BIBM.2018.8621473.","DOI":"10.1109\/BIBM.2018.8621473"},{"key":"3898_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-815392-5.00005-8","author":"N Kulkarni","year":"2018","unstructured":"Kulkarni, N., and V. Bairagi. EEG-based diagnosis of alzheimer disease: a review and novel approaches for feature extraction and classification techniques. 2018. https:\/\/doi.org\/10.1016\/B978-0-12-815392-5.00005-8.","journal-title":"EEG-based diagnosis of alzheimer disease: a review and novel approaches for feature extraction and classification techniques."},{"key":"3898_CR57","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3347032","author":"MNA Tawhid","year":"2023","unstructured":"Tawhid, M. N. A., S. Siuly, E. Kabir, and Y. Li. Exploring frequency band-based biomarkers of EEG signals for mild cognitive impairment detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023. https:\/\/doi.org\/10.1109\/TNSRE.2023.3347032.","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"3898_CR58","doi-asserted-by":"publisher","unstructured":"Andrade, K., et al., Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly. Journal of Integrative Neuroscience, 2024. 23(3): p. 67. https:\/\/doi.org\/10.31083\/j.jin2303067.","DOI":"10.31083\/j.jin2303067"},{"key":"3898_CR59","doi-asserted-by":"publisher","unstructured":"Ieracitano, C., et al. A time-frequency based machine learning system for brain states classification via EEG signal processing. In the 2019 International Joint Conference on Neural Networks (IJCNN). 2019. IEEE. https:\/\/doi.org\/10.1109\/IJCNN.2019.8852240.","DOI":"10.1109\/IJCNN.2019.8852240"},{"key":"3898_CR60","doi-asserted-by":"publisher","unstructured":"Gevins A, Smith ME, McEvoy LK, Leong H, Le J., Electroencephalographic imaging of higher brain function. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 1999. 354(1387): p. 1125-1134. https:\/\/doi.org\/10.1098\/rstb 1999.0468.","DOI":"10.1098\/rstb"},{"key":"3898_CR61","doi-asserted-by":"publisher","unstructured":"Salis, C.I., E. Malissavas, A. Bizopoulos, P., T. Tzallas, A., Angelidis, P.A., G. Tsalikakis, D., Denoising simulated EEG signals: A comparative study of EMD, wavelet transform, and Kalman filter. In the 13th IEEE International Conference on BioInformatics and BioEngineering. 2013. IEEE. https:\/\/doi.org\/10.1109\/BIBE.2013.6701613.","DOI":"10.1109\/BIBE.2013.6701613"},{"key":"3898_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102337","volume":"65","author":"C Kaur","year":"2021","unstructured":"Kaur, C., A. Bisht, P. Singh, and G. Joshi. EEG Signal denoising using a hybrid approach of Variational Mode Decomposition and wavelets for depression. Biomedical Signal Processing and Control.65:102337, 2021. https:\/\/doi.org\/10.1016\/j.bspc.2020.102337.","journal-title":"Biomedical Signal Processing and Control"},{"key":"3898_CR63","doi-asserted-by":"publisher","unstructured":"Mamatha, G. and S. Hariprasad, Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal. Indian Journal of Science and Technology, 2024. 17(23): p. 2370-2380. https:\/\/doi.org\/10.17485\/IJST\/v17i23.3.","DOI":"10.17485\/IJST\/v17i23.3"},{"key":"3898_CR64","doi-asserted-by":"publisher","unstructured":"Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers. Journal of Machine Learning Research, 2008. 9(9). https:\/\/doi.org\/10.1145\/1390681.1442799.","DOI":"10.1145\/1390681.1442799"},{"issue":"1","key":"3898_CR65","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.cmpb.2011.11.005","volume":"108","author":"L Fraiwan","year":"2012","unstructured":"Fraiwan, L., K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus. Automated sleep stage identification system based on time\u2013frequency analysis of a single EEG channel and random forest classifier. Computer methods and programs in biomedicine. 108(1):10\u201319, 2012. https:\/\/doi.org\/10.1016\/j.cmpb.2011.11.005.","journal-title":"Computer methods and programs in biomedicine"},{"issue":"4","key":"3898_CR66","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/brainsci9040081","volume":"9","author":"KD Tzimourta","year":"2019","unstructured":"Tzimourta, K. D., et al. EEG window length evaluation for the detection of Alzheimer\u2019s disease over different brain regions. Brain Sciences. 9(4):81, 2019. https:\/\/doi.org\/10.3390\/brainsci9040081.","journal-title":"Brain Sciences"},{"key":"3898_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102223","volume":"63","author":"B Oltu","year":"2021","unstructured":"Oltu, B., M. F. Ak\u015fahin, and S. Kibaro\u011flu. A novel electroencephalography-based approach for Alzheimer\u2019s disease and mild cognitive impairment detection. Biomedical Signal Processing and Control.63:102223, 2021. https:\/\/doi.org\/10.1016\/j.bspc.2020.102223.","journal-title":"Biomedical Signal Processing and Control"},{"key":"3898_CR68","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.cmpb.2017.03.006","volume":"143","author":"F Previtali","year":"2017","unstructured":"Previtali, F., et al. A novel method and software for automatically classifying Alzheimer\u2019s disease patients by magnetic resonance imaging analysis. Computer methods and programs in biomedicine. 143:89\u201395, 2017. https:\/\/doi.org\/10.1016\/j.cmpb.2017.03.006.","journal-title":"Computer methods and programs in biomedicine"},{"issue":"1","key":"3898_CR69","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neurobiolaging.2013.06.018","volume":"35","author":"PT Trzepacz","year":"2014","unstructured":"Trzepacz, P. T., et al. Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer\u2019s dementia. Neurobiology of aging. 35(1):143\u2013151, 2014. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2013.06.018.","journal-title":"Neurobiology of aging"},{"issue":"5646","key":"3898_CR70","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1126\/science1090349","volume":"302","author":"ST DeKosky","year":"2003","unstructured":"DeKosky, S. T., and K. Marek. Looking backward to move forward: early detection of neurodegenerative disorders. Science. 302(5646):830\u2013834, 2003. https:\/\/doi.org\/10.1126\/science1090349.","journal-title":"Science"},{"issue":"1","key":"3898_CR71","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.psychres.2010.04.058","volume":"186","author":"SM Snyder","year":"2011","unstructured":"Snyder, S. M., et al. Addition of EEG improves the accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI. Psychiatry Research. 186(1):97\u2013102, 2011. https:\/\/doi.org\/10.1016\/j.psychres.2010.04.058.","journal-title":"Psychiatry Research"},{"issue":"1","key":"3898_CR72","doi-asserted-by":"publisher","first-page":"S137","DOI":"10.1016\/j.jalz.2007.10.008","volume":"4","author":"CE Jackson","year":"2008","unstructured":"Jackson, C. E., and P. J. Snyder. Electroencephalography and event-related potentials as biomarkers of mild cognitive impairment and mild Alzheimer\u2019s disease. Alzheimer\u2019s & Dementia. 4(1):S137\u2013S143, 2008. https:\/\/doi.org\/10.1016\/j.jalz.2007.10.008.","journal-title":"Alzheimer's & Dementia"},{"issue":"8","key":"3898_CR73","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1016\/S1388-2457(01)00579-X","volume":"112","author":"A Petrosian","year":"2001","unstructured":"Petrosian, A., et al. Recurrent neural network-based approach for early recognition of Alzheimer\u2019s disease in EEG. Clinical Neurophysiology. 112(8):1378\u20131387, 2001. https:\/\/doi.org\/10.1016\/S1388-2457(01)00579-X.","journal-title":"Clinical Neurophysiology"},{"key":"3898_CR74","doi-asserted-by":"publisher","unstructured":"Sabbagh, M.N., et al., Rationale for early diagnosis of mild cognitive impairment (MCI) supported by emerging digital technologies. The journal of prevention of Alzheimer's disease, 2020. 7(3): p. 158-164. https:\/\/doi.org\/10.14283\/jpad.2020.19.","DOI":"10.14283\/jpad.2020.19"}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-025-03898-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-025-03898-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-025-03898-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T14:24:22Z","timestamp":1769610262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10439-025-03898-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"references-count":74,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["3898"],"URL":"https:\/\/doi.org\/10.1007\/s10439-025-03898-6","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]},"assertion":[{"value":"7 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors (Saba Haji Molla Rabie, Sedigheh Ghofrani, Mahdi Eslami, and Hamideh Barghamadi) declare that they have no conflicts of interest or personal relationships that could have influenced the work presented in this paper. The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. This manuscript has not been submitted to, nor is it under review at, another journal or other publishing venue.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}